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A scenario aggregation based approach for determining a robust airline fleet composition

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  • Listes, O.L.
  • Dekker, R.

Abstract

Strategic airline fleet planning is one of the major issues addressed through newly initiated decision support systems, designed to assist airlines and aircraft manufacturers in assessing the benefits of the emerging concept of dynamic capacity allocation. We present background research connected with such a system, which aims to explicitly account for the stochastic nature of passenger demand in supporting decisions related to the fleet composition problem. We address this problem through a scenario aggregation based approach and present results on representative case studies based on realistic data. Our investigations establish clear benefits of a stochastic approach as compared with deterministic formulations, as well as its implementation feasibility using state-of-the-art optimization software.

Suggested Citation

  • Listes, O.L. & Dekker, R., 2002. "A scenario aggregation based approach for determining a robust airline fleet composition," Econometric Institute Research Papers EI 2002-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:570
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    References listed on IDEAS

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    Cited by:

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    3. Lee, Loo Hay & Lee, Chul Ung & Tan, Yen Ping, 2007. "A multi-objective genetic algorithm for robust flight scheduling using simulation," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1948-1968, March.
    4. Jean-Paul Watson & David Woodruff, 2011. "Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems," Computational Management Science, Springer, vol. 8(4), pages 355-370, November.
    5. Pilla, Venkata L. & Rosenberger, Jay M. & Chen, Victoria & Engsuwan, Narakorn & Siddappa, Sheela, 2012. "A multivariate adaptive regression splines cutting plane approach for solving a two-stage stochastic programming fleet assignment model," European Journal of Operational Research, Elsevier, vol. 216(1), pages 162-171.

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